- require 'zipf'
!!!
%html
  %head
    %title Debug view (Session ##{session_key})
  %link(rel="stylesheet" type="text/css" href="debug.css")
  %script{:src =>"http://ajax.googleapis.com/ajax/libs/jquery/1.11.2/jquery.min.js", :charset=>"utf-8"}
  %script{:src => "http://postedit.cl.uni-heidelberg.de/js/debug.js"}
  %body
    %h1 Debug view
    %h2 Session [##{session_key}]
    - if pairwise_ranking_data["kbest"].empty?
      %p.red
        %strong No data to show!
    %ul
      %li
        %a{ :href => "#controls" } Controls
      %li
        %a{ :href => "#post_edit" } Post-edit & machine translation
      %li
        %a{ :href => "#meta" } Meta
      %li
        %a{ :href => "#rules" } Learned rules
      %li
        %a{ :href => "#pairwise_ranking" } Pairwise ranking
    %hr
    /=#########################################################################
    %h2#controls Controls
    %h3 Reset
    %p
      %strong [Server reply]
      %span#control_reply
    %ul
      %li
        %a.ajax{:tgt => "/reset_progress",  :href => "#controls"} Reset progress
      %li
        %a.ajax{:tgt => "/reset_weights",   :href => "#controls"} Reset weights
      %li
        %a.ajax{:tgt => "/reset_extractor", :href => "#controls"} Reset extractor
      %li
        %a.ajax{:tgt => "/reset_new_rules", :href => "#controls"} Reset new rules
      %li
        %a.ajax{:tgt => "/shutdown",        :href => "#controls"} Initiate shutdown
    %h3 Learning rate
    %p Default for dense features: <strong>1.0e-05</strong>, for sparse features: <strong>1.0e-05</strong>
    %select
      - [1000,100,10,1,0.1,0.01,0.001,0.0001,0.00001,0.000001,0.0000001,0.00000001,0.000000001,0.0000000001].each do |i|
        %option.ajax{:value => i, :tgt => "/set_learning_rate/#{i}"} #{i}
    %em dense features <br />
    %select
      - [1000,100,10,1,0.1,0.01,0.001,0.0001,0.00001,0.000001,0.0000001,0.00000001,0.000000001,0.0000000001].each do |i|
        %option.ajax{:value => i, :tgt => "/set_learning_rate/sparse/#{i}"} #{i}
    %em sparse features
    %p
      %a{ :href => "#" } ^ up
    %hr
    /=#########################################################################
    %h2#post_edit Post-edit &amp; machine translation
    %p#original_svg_b64 #{data["original_svg"][progress]}
    %p#svg_b64 #{data["svg"][progress]}
    %div#original_svg
    %div#svg
    %table
      %tr
        %td.noborder
          %strong progress:
        %td.left #{[0,progress].max}
      %tr
        %td.noborder
          %strong MT Input
        %td.left #{data["source_segments"][[0,progress].max]}
      %tr
        %td.noborder
          %strong Raw source
        %td.left #{data["raw_source_segments"][[0,progress].max]}
      %tr
        %td.noborder
          %strong Post-edit
        %td.left #{data["post_edits_raw"][progress]}
      %tr
        %td.noborder
          %strong Post-edit (processed)
        %td.left #{data["post_edits"][progress]}
      %tr
        %td.noborder
          %strong Original MT
        %td.left #{data["mt_raw"][progress]}
      %tr
        %td.noborder
          %strong Displayed MT
        %td.left #{data["mt"][progress]}
      %tr
        %td.noborder
          %strong Best match (BLEU=#{(pairwise_ranking_data["best_match_score"]*100).round(2)}%)
        %td.left #{pairwise_ranking_data["best_match"]}
    %h2 Derivation
    %pre #{data["derivations"][progress]}
    /= %h3 Processed
    /= - if data["derivations_proc"][progress]
    /=  %pre #{JSON.pretty_generate(JSON.parse(data["derivations_proc"][progress]))}
    %h2 Client reply
    - if data["feedback"][progress]
      %pre #{JSON.pretty_generate(JSON.parse(data["feedback"][progress]))}
    %p
      %a{ :href => "#" } ^ up
    %hr
    /=#########################################################################
    %h2#meta Meta
    %p <strong>k:</strong> #{pairwise_ranking_data["samples_size"]}
    %p <strong>number of updates:</strong> #{pairwise_ranking_data["num_up"]}
    %p <strong>updated features:</strong> #{pairwise_ranking_data["updated_features"]}
    %p <strong>learning rate:</strong> #{pairwise_ranking_data["learning_rate"]}
    %p <strong>learning rate (sparse):</strong> #{pairwise_ranking_data["learning_rate_sparse"]}
    %p <strong>duration:</strong> #{data["durations"][progress]}ms
    %p <strong>updated:</strong> #{data["updated"][progress]}
    %p
      %a{ :href => "#" } ^ up
    %hr
    /=#########################################################################
    %h2#rules New &amp; known rules
    %pre #{additional_rules.join("\n")}
    %h3
      Known rules
    %pre #{rejected_rules.join("\n")}
    %p
      %a{ :href => "#" } ^ up
    %hr
    /=#########################################################################
    %h2#pairwise_ranking Pairwise ranking updates
    %h3 K-best list
    %p (Empty or list from previous updates displayed if there was no change in the current post-edit.)
    %pre [BLEU score | Model score | Original rank | \|e\| | Translation \n Features]
    %p.red In red: Update needed, i.e. "any of the above hypotheses has a lower model score"
    %ol
      - kbest = []
      - pairwise_ranking_data["kbest"].each { |i| x=splitpipe(i); kbest << [ x[0].to_f, x[1].to_f, x[2].to_i, x[3], x[4] ] }
      - kbest.sort! { |i,j| j[0] <=> i[0] }
      - kbest.each_with_index do |k,j|
        - b = kbest[0,j].map { |l| l[0]>k[0] && l[1]<k[1] }.include? true
        -if b
          %li.red
            %strong #{"%.2f"%(k[0].to_f*100)} | #{k[1]} | #{k[2]} | #{k[4].split.size} |#{k[4]} <br/>
            %p{:style=>"font-size:80%"} #{k[3]}
        - else
          %li
            %strong #{"%.2f"%(k[0].to_f*100)} | #{k[1]} | #{k[2]} | #{k[4].split.size} | #{k[4]} <br/>
            %p{:style=>"font-size:80%"} #{k[3]}
        - if [9,89].include? j
          %hr
    %h3 Weight updates
    %p Raw update: #{pairwise_ranking_data["update_raw"]}
    /=%p Update (learning rates applied): <pre>#{pairwise_ranking_data["update"]}</pre>
    /=- "#{pairwise_ranking_data["update"]}"
    %table
      %tr
        %th Feature
        %th Before
        %th After
        %th Diff.
        %th Raw diff.
      - pairwise_ranking_data["weights_before"].default = 0
      - pairwise_ranking_data["weights_after"].keys.each.sort { |a,b| a[0] <=> b[0] }.each do |k|
        %tr
          %td.noborder <strong> #{k} </strong>
          %td #{"%+.3f"%pairwise_ranking_data["weights_before"][k].round(4)}
          %td #{"%+.3f"%pairwise_ranking_data["weights_after"][k].round(4)}
          - diff = pairwise_ranking_data["weights_after"][k]-pairwise_ranking_data["weights_before"][k]
          - if diff < 0
            %td.red #{"%+.3f"%(diff).round(4)}
          - elsif diff > 0
            %td.green #{"%+.3f"%(diff).round(4)}
          - else
            %td #{"%+.3f"%(diff).round(4)}
          - if !k.start_with? "R:"
            %td #{"%+.1f"%((pairwise_ranking_data["weights_after"][k]-pairwise_ranking_data["weights_before"][k])/pairwise_ranking_data["learning_rate"]).round(2)}
          - else
            %td #{"%+.1f"%((pairwise_ranking_data["weights_after"][k]-pairwise_ranking_data["weights_before"][k])/pairwise_ranking_data["learning_rate_sparse"]).round(2)}
    %h3 Features explained
    %table
      %tr
        %td.noborder EgivenFCoherent (rule)
        %td.left -log10[ c(e, f) / sample c(f) ]
      %tr
        %td.noborder NewRule (rule)
        %td.left Only feature of additional rules, weight fixed at 1
      %tr
        %td.noborder KnownRule (rule)
        %td.left Added to existing rules if they could be extracted from previous post-edits
      %tr
        %td.noborder OOVFix (rule)
        %td.left Manually added rules to fix OOV words
      %tr
        %td.noborder Glue
        %td.left Absolute number of rules used from glue grammar
      %tr
        %td.noborder IsSingletonF/E (rule)
        %td.left true|false (1|0) (sum)
      %tr
        %td.noborder IsSingletonFE (rule)
        %td.left true|false (1|0) (sum)
      %tr
        %td.noborder LanguageModel
        %td.left -log10[ score ]
      %tr
        %td.noborder LanguageModel_OOV
        %td.left Abs. count of OOV unigrams
      %tr
        %td.noborder MaxLexFgivenE (rule)
        %td.left Sum_f -log10(maxScore) (maxScore = max_e(ttable(f))
      %tr
        %td.noborder MaxLexEgivenF (rule)
        %td.left Sum_e -log10(maxScore) (maxScore = max_f(ttable(e))
      %tr
        %td.noborder PassThrough
        %td.left Absolute count of used PassThrough rules (1 per word)
      %tr
        %td.noborder SampleCountF (rule)
        %td.left log10 [ sample c(f) ]
      %tr
        %td.noborder WordPenalty
        %td.left log_10(e)*|e| = 1/log(10) * |e| (*-1) = -0.43429448190325176*|e|
      %tr
        %td.noborder SourceWordPenalty (per edge/rule)
        %td.left ^^^ (|e| <=> |f|)
      %tr
        %td.noborder R:*
        %td.left Rule indicator features, sum over full derivation per rule
      %tr
        %td.noborder Shape_*
        %td.left Indicator features for rule shapes
      %tr
        %td.noborder IsSupportedOnline
        %td.left Counts how many rules have support from local context (Denkowski)
  %p
    %a{ :href => "#" } ^ up